What metrics would you use to evaluate Starburst's cross-cloud query performance?

Introduction

Evaluating Starburst's cross-cloud query performance is crucial for ensuring optimal data analytics capabilities across diverse cloud environments. To approach this product success metrics problem effectively, I'll follow a structured framework that covers core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Starburst is a distributed SQL query engine that enables analytics across multiple data sources and cloud platforms. It's built on the open-source Trino project (formerly PrestoSQL) and designed for high-performance, large-scale data processing.

Key stakeholders include:

1. Data analysts and scientists: Seeking fast, efficient querying across diverse data sources
2. IT and infrastructure teams: Concerned with resource utilization and cost management
3. Business leaders: Interested in deriving timely insights from company-wide data
4. Cloud providers: Partnering to optimize performance on their platforms

User flow typically involves:

1. Connecting to various data sources across different clouds
2. Writing and submitting SQL queries
3. Receiving and analyzing query results

Starburst fits into the broader strategy of enabling a unified data analytics layer across hybrid and multi-cloud environments. It competes with solutions like Snowflake and Google BigQuery, differentiating through its ability to query data in-place without moving it to a central repository.

In terms of product lifecycle, Starburst is in the growth stage, rapidly expanding its feature set and customer base as organizations increasingly adopt multi-cloud strategies.